AKT as locus of cancer multidrug resistance and fragility

Complexity and robustness of cancer hypoxic microenvironment are supported by the robust signaling networks of autocrine and paracrine elements creating powerful interactome for multidrug resistance. These elements generate a positive feedback loops responsible for the extreme robustness and multidrug resistance in solid cancer, leukemia, myeloma, and lymphoma. Phosphorylated AKT is a cancer multidrug resistance locus. Targeting that locus by oxidant/antioxidant balance modulation, positive feedback loops are converted into negative feedback loops, leading to disappearance of multidrug resistance. This is a new principle for targeting cancer multidrug resistance by the locus chemotherapy inducing a phenomenon of loops conversion. J. Cell. Physiol. 228: 671–674, 2013. © 2012 Wiley Periodicals, Inc.     

AKT as locus of cancer angiogenic robustness and fragility

Angiogenesis get full robustness in metastatic cancer, relapsed leukemia or lymphoma when complex positive feedback loop signaling systems become integrative. A cancer hypoxic microenvironment generates positive loops inducing formation of the vascular functional shunts. AKT is an upstream angiogenic locus of integrative robustness and fragility activated by the positive loops. AKT controls two downstream nodes the mTOR and NOS in nodal organization of the signaling genes. AKT phosphorylation is regulated by a balance of an oxidant/antioxidant. Targeting AKT locus represents new principle to control integrative angiogenic robustness by the locus chemotherapy. J. Cell. Physiol. 228: 21–24, 2013. © 2012 Wiley Periodicals, Inc.     

AKT as Locus of Hydrogen Bond Network in Cancer

Generation and maintenance of a cancer complexity and robustness are impossible without hydrogen element. It is essential element for the cancer signaling through the AKT locus. Hyperactivated AKT locus by a positive feedback loops from the cancer hypoxic microenvironment generates a hydrogen bond network. Such network initiates protein–protein interaction at the AKT active site and at the same time stabilizes signal propagation. A hydrogen bond network conforms an entropy/enthalpy energetic process used for the interconversion of the AKT protein in metastasis formation and maintenance. Targeting the AKT locus by the redox balance change or hydrogen balance change or proton beam radiation disrupts a hydrogen bond network leading to the disappearance of a cancer complexity and robustness causing failure of the complex energy system in solid cancers and hematological malignancy. J. Cell. Biochem. 119: 130–133, 2018. © 2017 Wiley Periodicals, Inc.     

Muon disrupts AKT hydrogen bond network in cancer

A cancer microenvironment generates strong hydrogen bond network system by the positive feedback loops supporting cancer complexity and robustness. Such network functions through the AKT locus generating high entropic energy supporting cancer metastatic robustness. Charged lepton particle muon follows the rule of Bragg effect during a collision with hydrogen network in cancer cells. Muon beam dismantles hydrogen bond network in cancer by the muon-catalyzed fusion, leading to apoptosis of cancer cells. Muon induces cumulative energy appearance on the hydrogen bond network in a cancer cell with its fast decay to an electron and two neutrinos. Thus, muon beam, muonic atom, muon neutrino shower, and electrons simultaneously cause fast neutralization of the AKT hydrogen bond network by the conversion of hydrogen into deuterium or helium, inactivating the hydrogen bond networks and inducing failure of cancer complexity and robustness with the disappearance of a malignant phenotype.     

AKT as locus of fragility in robust cancer system

Metastatic cancer is a complex positive feedback loop system. Such as system has a tendency to acquire extreme robustness. Signaling pathways controlling that robustness can fail completely if an essential element from the signaling is removed. That element is a locus of fragility. Targeting that locus represents the best way to target the cancer robustness. This prospect presents another locus of fragility in signaling complex system network, controlling the cell cycle progression through the PI3K/AKT/mTOR/RAN pathway and cell migration and angiogenesis through the VEGF/PI3K/AKT/NO/ICAM-1 pathway. The locus of fragility of these pathways is AKT, which is regulated by a balance of catalase/H2O2 or by AKT inhibitor. Tiny and trivial perturbations such as change in redox state in the cells by antioxidant enzyme catalase, scavenging H2O2 signaling molecule, regulates robust signaling molecule AKT, abolishing its phosporilation and inducing cascading failure of robust signaling pathways for cell growth, proliferation, migration, and angiogenesis. An anticancer effect of the antioxidant is achieved through the AKT locus, by abolishing signals from growth factors VEGF, HGF, HIF-1alpha and H2O2. Previously reported locus of fragility nitric oxide (NO) and locus AKT are close in the complex signaling interactome network, but they regulate distinct signaling modules. Simultaneously targeted loci represents new principles in cancer robustness chemotherapy by blocking cell proliferation, migration, angiogenesis and inducing rather slow then fast apoptosis leading to slow eradication of cancer.     

Coherent coupling of feedback loops: a design principle of cell signaling networks

MOTIVATION: It is widely accepted that cell signaling networks have been evolved to be robust against perturbations. To investigate the topological characteristics resulting in such robustness, we have examined large-scale signaling networks and found that a number of feedback loops are present mostly in coupled structures. In particular, the coupling was made in a coherent way implying that same types of feedback loops are interlinked together. RESULTS: We have investigated the role of such coherently coupled feedback loops through extensive Boolean network simulations and found that a high proportion of coherent couplings can enhance the robustness of a network against its state perturbations. Moreover, we found that the robustness achieved by coherently coupled feedback loops can be kept evolutionarily stable. All these results imply that the coherent coupling of feedback loops might be a design principle of cell signaling networks devised to achieve the robustness.     

Robustness in phenotypic plasticity and heterogeneity patterns enabled by EMT networks

Epithelial-mesenchymal plasticity (EMP) is a key arm of cancer metastasis and is observed across many contexts. Cells undergoing EMP can reversibly switch between three classes of phenotypes: epithelial (E), mesenchymal (M), and hybrid E/M. While a large number of multistable regulatory networks have been identified to be driving EMP in various contexts, the exact mechanisms and design principles that enable robustness in driving EMP across contexts are not yet fully understood. Here, we investigated dynamic and structural robustness in EMP networks with regard to phenotypic heterogeneity and plasticity. We use two different approaches to simulate these networks: a computationally inexpensive, parameter-independent continuous state space Boolean model, and an ODE-based parameter-agnostic framework (RACIPE), both of which yielded similar phenotypic distributions. While the latter approach is useful for measurements of plasticity, the former model enabled us to extensively investigate robustness in phenotypic heterogeneity. Using perturbations to network topology and by varying network parameters, we show that multistable EMP networks are structurally and dynamically more robust compared with their randomized counterparts, thereby highlighting their topological hallmarks. These features of robustness are governed by a balance of positive and negative feedback loops embedded in these networks. Using a combination of the number of negative and positive feedback loops weighted by their lengths, we identified a metric that can explain the structural and dynamical robustness of these networks. This metric enabled us to compare networks across multiple sizes, and the network principles thus obtained can be used to identify fragilities in large networks without simulating their dynamics. Our analysis highlights a network topology-based approach to quantify robustness in the phenotypic heterogeneity and plasticity emergent from EMP networks.     

Overlaid positive and negative feedback loops shape dynamical properties of PhoPQ two-component system

Bacteria use two-component systems (TCSs) to sense environmental conditions and change gene expression in response to those conditions. To amplify cellular responses, many bacterial TCSs are under positive feedback control, i.e. increase their expression when activated. Escherichia coli Mg²⁺ -sensing TCS, PhoPQ, in addition to the positive feedback, includes a negative feedback loop via the upregulation of the MgrB protein that inhibits PhoQ. How the interplay of these feedback loops shapes steady-state and dynamical responses of PhoPQ TCS to change in Mg²⁺ remains poorly understood. In particular, how the presence of MgrB feedback affects the robustness of PhoPQ response to overexpression of TCS is unclear. It is also unclear why the steady-state response to decreasing Mg²⁺ is biphasic, i.e. plateaus over a range of Mg²⁺ concentrations, and then increases again at growth-limiting Mg²⁺. In this study, we use mathematical modeling to identify potential mechanisms behind these experimentally observed dynamical properties. The results make experimentally testable predictions for the regime with response robustness and propose a novel explanation of biphasic response constraining the mechanisms for modulation of PhoQ activity by Mg²⁺ and MgrB. Finally, we show how the interplay of positive and negative feedback loops affects the network’s steady-state sensitivity and response dynamics. In the absence of MgrB feedback, the model predicts oscillations thereby suggesting a general mechanism of oscillatory or pulsatile dynamics in autoregulated TCSs. These results improve the understanding of TCS signaling and other networks with overlaid positive and negative feedback.     

Lysosome activates AKT inducing cancer and metastasis

Hyperactivated lysosome causes cancer and induces metastasis or cancer relapse. Such activation occurs during excessive, intense, and protracted oxidative burst in the lysosome. The burst induces the formation of the constitutively active (permanently active) AKT locus generating cancer complexity and robustness. Such condition has the tendency to persist by stabilized intense signaling inducing upregulation of cell function and metabolic setup at the higher level. Most intense activator of the lysosome is the fungus Aspergillus fumigatus, which activates the AKT, a critical element in lysosome control, inducing cancer development, metastatic progression, or cancer relapse. Targeting the AKT active site of hydrogen network, by redox balance change or hydrogen balance change or muon-catalyzed fusion or laser-induced fusion with anti- A. fumigatus medication converts active AKT locus into inactive element, inducing disappearance of malignant phenotype.     

Singularity analysis of the AKT signaling pathway reveals connections between cancer and metabolic diseases

Connections between cancer and metabolic diseases may consist in the complex network of interactions among a common set of biomolecules. By applying singularity and bifurcation analysis, the phenotypes constrained by the AKT signaling pathway are identified and mapped onto the parameter space, which include cancer and certain metabolic diseases. By considering physiologic properties (sensitivity, robustness and adaptivity) the AKT pathway must possess in order to efficiently sense growth factors and nutrients, the region of normal responses is located. To optimize these properties, the intracellular concentration of the AKT protein must be sufficiently high to saturate its enzymes; the strength of the positive feedback must be stronger than that of the negative feedback. The analysis illuminates the parameter space and reveals system-level mechanisms in regulating biological functions (cell growth, survival, proliferation and metabolism) and how their deregulation may lead to the development of diseases. The analytical expressions summarize the synergistic interactions among many molecules, which provides valuable insights into therapeutic interventions. In particular, a strategy for overcoming the limitations of mTOR inhibition is proposed for cancer therapy.     

Biology using engineering tools

Negative feedback is an ubiquitous feature of biological networks. Recent work from Sturm and colleaguespresents experimental evidence that biological negative feedback can serve the same function as it does for engineered systems: robustness to perturbations within the feedback loop. Such behavior has important implications for how to attack deregulated signaling networks containing negative feedback in diseases such as cancer.     

Oscillations in MAPK cascade triggered by two distinct designs of coupled positive and negative feedback loops

ABSTRACT: BACKGROUND: Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs. RESULTS: We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case. CONCLUSION: The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.     

Differential roles of ERRFI1 in EGFR and AKT pathway regulation affect cancer proliferation

AKT signaling is modulated by a complex network of regulatory proteins and is commonly deregulated in cancer. Here, we present a dual mechanism of AKT regulation by the ERBB receptor feedback inhibitor 1 (ERRFI1). We show that in cells expressing high levels of EGFR, ERRF1 inhibits growth and enhances responses to chemotherapy. This is mediated in part through the negative regulation of AKT signaling by direct ERRFI1‐dependent inhibition of EGFR. In cells expressing low levels of EGFR, ERRFI1 positively modulates AKT signaling by interfering with the interaction of the inactivating phosphatase PHLPP with AKT, thereby promoting cell growth and chemotherapy desensitization. These observations broaden our understanding of chemotherapy response and have important implications for the selection of targeted therapies in a cell context‐dependent manner. EGFR inhibition can only sensitize EGFR‐high cells for chemotherapy, while AKT inhibition increases chemosensitivity in EGFR‐low cells. By understanding these mechanisms, we can take advantage of the cellular context to individualize antineoplastic therapy. Finally, our data also suggest targeting of EFFRI1 in EGFR‐low cancer as a promising therapeutic approach.     

Network quantification of EGFR signaling unveils potential for targeted combination therapy

The epidermal growth factor receptor (EGFR) signaling network is activated in most solid tumors, and small‐molecule drugs targeting this network are increasingly available. However, often only specific combinations of inhibitors are effective. Therefore, the prediction of potent combinatorial treatments is a major challenge in targeted cancer therapy. In this study, we demonstrate how a model‐based evaluation of signaling data can assist in finding the most suitable treatment combination. We generated a perturbation data set by monitoring the response of RAS/PI3K signaling to combined stimulations and inhibitions in a panel of colorectal cancer cell lines, which we analyzed using mathematical models. We detected that a negative feedback involving EGFR mediates strong cross talk from ERK to AKT. Consequently, when inhibiting MAPK, AKT activity is increased in an EGFR‐dependent manner. Using the model, we predict that in contrast to single inhibition, combined inactivation of MEK and EGFR could inactivate both endpoints of RAS, ERK and AKT. We further could demonstrate that this combination blocked cell growth in BRAF‐ as well as KRAS‐mutated tumor cells, which we confirmed using a xenograft model.     

Systems‐level interactions between insulin–EGF networks amplify mitogenic signaling

Crosstalk mechanisms have not been studied as thoroughly as individual signaling pathways. We exploit experimental and computational approaches to reveal how a concordant interplay between the insulin and epidermal growth factor (EGF) signaling networks can potentiate mitogenic signaling. In HEK293 cells, insulin is a poor activator of the Ras/ERK (extracellular signal‐regulated kinase) cascade, yet it enhances ERK activation by low EGF doses. We find that major crosstalk mechanisms that amplify ERK signaling are localized upstream of Ras and at the Ras/Raf level. Computational modeling unveils how critical network nodes, the adaptor proteins GAB1 and insulin receptor substrate (IRS), Src kinase, and phosphatase SHP2, convert insulin‐induced increase in the phosphatidylinositol‐3,4,5‐triphosphate (PIP₃) concentration into enhanced Ras/ERK activity. The model predicts and experiments confirm that insulin‐induced amplification of mitogenic signaling is abolished by disrupting PIP₃‐mediated positive feedback via GAB1 and IRS. We demonstrate that GAB1 behaves as a non‐linear amplifier of mitogenic responses and insulin endows EGF signaling with robustness to GAB1 suppression. Our results show the feasibility of using computational models to identify key target combinations and predict complex cellular responses to a mixture of external cues.     

Insights into the organization of biochemical regulatory networks using graph theory analyses

Graph theory has been a valuable mathematical modeling tool to gain insights into the topological organization of biochemical networks. There are two types of insights that may be obtained by graph theory analyses. The first provides an overview of the global organization of biochemical networks; the second uses prior knowledge to place results from multivariate experiments, such as microarray data sets, in the context of known pathways and networks to infer regulation. Using graph analyses, biochemical networks are found to be scale-free and small-world, indicating that these networks contain hubs, which are proteins that interact with many other molecules. These hubs may interact with many different types of proteins at the same time and location or at different times and locations, resulting in diverse biological responses. Groups of components in networks are organized in recurring patterns termed network motifs such as feedback and feed-forward loops. Graph analysis revealed that negative feedback loops are less common and are present mostly in proximity to the membrane, whereas positive feedback loops are highly nested in an architecture that promotes dynamical stability. Cell signaling networks have multiple pathways from some input receptors and few from others. Such topology is reminiscent of a classification system. Signaling networks display a bow-tie structure indicative of funneling information from extracellular signals and then dispatching information from a few specific central intracellular signaling nexuses. These insights show that graph theory is a valuable tool for gaining an understanding of global regulatory features of biochemical networks.     

Distinctive topologies of partner-switching signaling networks correlate with their physiological roles

Regulatory networks controlling bacterial gene expression often evolve from common origins and share homologous proteins and similar network motifs. However, when functioning in different physiological contexts, these motifs may be re-arranged with different topologies that significantly affect network performance. Here we analyze two related signaling networks in the bacterium Bacillus subtilis in order to assess the consequences of their different topologies, with the aim of formulating design principles applicable to other systems. These two networks control the activities of the general stress response factor sigma(B) and the first sporulation-specific factor sigma(F). Both networks have at their core a "partner-switching" mechanism, in which an anti-sigma factor forms alternate complexes either with the sigma factor, holding it inactive, or with an anti-anti-sigma factor, thereby freeing sigma. However, clear differences in network structure are apparent: the anti-sigma factor for sigma(F) forms a long-lived, "dead-end" complex with its anti-anti-sigma factor and ADP, whereas the genes encoding sigma(B) and its network partners lie in a sigma(B)-controlled operon, resulting in positive and negative feedback loops. We constructed mathematical models of both networks and examined which features were critical for the performance of each design. The sigma(F) model predicts that the self-enhancing formation of the dead-end complex transforms the network into a largely irreversible hysteretic switch; the simulations reported here also demonstrate that hysteresis and slow turn off kinetics are the only two system properties associated with this complex formation. By contrast, the sigma(B) model predicts that the positive and negative feedback loops produce graded, reversible behavior with high regulatory capacity and fast response time. Our models demonstrate how alterations in network design result in different system properties that correlate with regulatory demands. These design principles agree with the known or suspected roles of similar networks in diverse bacteria.     

Quantitative analysis of robustness and fragility in biological networks based on feedback dynamics

Motivation: It has been widely reported that biological networksare robust against perturbations such as mutations. On the contrary,it has also been known that biological networks are often fragileagainst unexpected mutations. There is a growing interest inthese intriguing observations and the underlying design principlethat causes such robust but fragile characteristics of biologicalnetworks. For relatively small networks, a feedback loop hasbeen considered as an important motif for realizing the robustness.It is still, however, not clear how a number of coupled feedbackloops actually affect the robustness of large complex biologicalnetworks. In particular, the relationship between fragilityand feedback loops has not yet been investigated till now. Results: Through extensive computational experiments, we foundthat networks with a larger number of positive feedback loopsand a smaller number of negative feedback loops are likely tobe more robust against perturbations. Moreover, we found thatthe nodes of a robust network subject to perturbations are mostlyinvolved with a smaller number of feedback loops compared withthe other nodes not usually subject to perturbations. This topologicalcharacteristic eventually makes the robust network fragile againstunexpected mutations at the nodes not previously exposed toperturbations. Contact: ckh{at}kaist.ac.kr Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Thomas Lengauer